Journal article
Application of machine learning to discover interactions predictive of dietary lapses
Applied psychology : health and well-being, v 15(3), pp 1166-1181
26 Dec 2022
PMID: 36573066
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The purpose of this study it to build a machine learning model to predict dietary lapses with comparable accuracy, sensitivity, and specificity to previous literature while recovering predictor interactions. The sample for the current study consisted of merged data from two separate studies of individuals with obesity/overweight (total N = 87). Participants completed six ecological momentary assessment surveys per day where they were asked about 16 risk factors of lapse and if they had lapsed from their dietary prescriptions since the previous survey. Alcohol consumption and self-efficacy were the most prevalent in the top 10 stable interactions. Alcohol consumption decreased the protective effect of self-efficacy, motivation, and planning. Higher planning predicted higher risk for lapse only when consuming alcohol. Low motivation, hunger, cravings, and lack of healthy food availability increased the protective effect of self-efficacy. Higher self-efficacy increased risk effect of positive mood and having recently eaten a meal on lapse. For individuals with lower levels of self-efficacy, planning increased the risk of lapse. Alcohol intake and self-efficacy interact with several variables to predict dietary lapses, and these interactions should be targeted in just-in-time adaptive interventions that deliver interventions for lapses.
Metrics
7 Record Views
Details
- Title
- Application of machine learning to discover interactions predictive of dietary lapses
- Creators
- Margaret Sala - Yeshiva UniversityAlexei Taylor - Drexel UniversityRebecca J Crochiere - Drexel UniversityFengqing Zhang - Drexel UniversityEvan M Forman - Drexel University
- Publication Details
- Applied psychology : health and well-being, v 15(3), pp 1166-1181
- Publisher
- Wiley
- Number of pages
- 16
- Grant note
- Karen Miller-Kovach research grant from Weight Watchers and The Obesity Society
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology); WELL Center
- Web of Science ID
- WOS:000904018400001
- Scopus ID
- 2-s2.0-85145324939
- Other Identifier
- 991019442585604721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Psychology, Applied